SOC And Capacity Estimation Based On The Dual Adaptive Particle Filters For Electric Vehicles

As one of the core components of electric vehicles,Battery management system can ensure the safe and efficient operation of power batteries.The Accurate SOC estimation is a necessary condition for efficient operation of the battery management system and is the necessary information for the driver of the electric vehicle.Because of non-linearity and time-varying,SOC initial error and unknown capacity value,SOC is difficult to estimate accurately.Accurate SOC and capacity estimation requires excellent performance algorithms and new methods,this paper based on the existence problems carried out the following work:Firstly,modeling of lithium battery,model selection and parameter identification are completed.In this paper,In this paper,we choose the equivalent circuit model with simple model structure and good adaptability.Nine kinds of widely used equivalent circuit models are established,and nine kinds of models were identified by the particle swarm optimization algorithm under the training data.Finally,through the comprehensive performance evaluation,select the resistance-capacitance network based one-state hysteresis equivalent circuit model as the best models for the two kinds of battery.In view of the problem that the general algorithm has poor adaptability to the non-Gaussian noise distribution or nonlinear systems,the particle filter algorithm is used to estimate the SOC of the power battery in this paper,and in view of the problem of poor robustness of particle filter,an improved adaptive particle filter method is proposed in this paper.The new method has a high degree of regulation for the state estimation error caused by the non-model error,and has a very significant effect on the SOC convergence rate especially for the case where the initial SOC error is large.In order to evaluate the performance of the proposed algorithm,the traditional adaptive algorithm and the improved adaptive algorithm are verified by the experimental data of different working conditions of the two kinds of batteries.Finally,the robustness of the improved algorithm is significantly improved.In this paper,a dual particle filtering algorithm is proposed to realize the joint estimation of the battery parameters and SOC,and in order to reduce the computational complexity of the algorithm,a multi-scale method is used to estimate the SOC and parameter,In order to verify the effectiveness of the algorithms,the two algorithms are validated by using the battery data under different health states.The results show that the dual particle filtering algorithm can achieve accurate SOC estimation,while the dual adaptive particle filter can obtain more accuracy SOC estimation than the former,and the time of SOC convergence of the dual adaptive particle filter is much shorter than the former.In order to solve the problem of capacity estimation of power battery,the mathematical relationship between capacity and SOC-OCV is established in this paper,So that the changes among the capacity,SOC and battery parameters have a mutually constrained relationship,then,the estimation of capacity is added to the algorithm of dual adaptive particle filter,which realizes the accurate estimation of the capacity.After the verification of the algorithm,the final results show that the algorithm can accurately estimate the capacity and SOC.